scholarly journals Wavefront Restoration Technology of Dynamic Non-Uniform Intensity Distribution Based on Extreme Learning Machine

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3877
Author(s):  
Haiqi Lin ◽  
Xing He ◽  
Shuai Wang ◽  
Ping Yang
Keyword(s):  
Root Mean Square Error ◽  
Extreme Learning Machine ◽  
Intensity Distribution ◽  
Near Field ◽  
Wavefront Sensor ◽  
Mean Square ◽  
Initial Value ◽  
Machine Method ◽  
Simulation Results ◽  
Learning Machine

Non-uniform intensity distribution of laser near-field beam results in the irregular shape of the spot in the wavefront sensor. The intensity of some sub-aperture spots may be too weak to be detected, and the accuracy of wavefront restoration is seriously affected. Therefore, an extreme learning machine method is proposed to realize high precision wavefront restoration under dynamic non-uniform intensity distribution. The simulation results show that this method has better accuracy of wavefront restoration than the classical modal algorithm under dynamic non-uniform intensity distribution. The root mean square error of the residual wavefront for the proposed method is only 2.9% of the initial value.

scholarly journals Prediction and Stability Assessment of Soft Foundation Settlement of the Fishbone-Shaped Dike Near the Estuary of the Yangtze River Using Machine Learning Methods

2021 ◽  
Vol 13 (7) ◽  
pp. 3744
Author(s):  
Mingcheng Zhu ◽  
Shouqian Li ◽  
Xianglong Wei ◽  
Peng Wang
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Models ◽  
Good Prediction ◽  
Foundation Settlement ◽  
Machine Method ◽  
Settlement Prediction ◽  
Soft Foundation ◽  
Simulation Results ◽  
Learning Machine ◽  
The Stability

Fishbone-shaped dikes are always built on the soft soil submerged in the water, and the soft foundation settlement plays a key role in the stability of these dikes. In this paper, a novel and simple approach was proposed to predict the soft foundation settlement of fishbone dikes by using the extreme learning machine. The extreme learning machine is a single-hidden-layer feedforward network with high regression and classification prediction accuracy. The data-driven settlement prediction models were built based on a small training sample size with a fast learning speed. The simulation results showed that the proposed methods had good prediction performances by facilitating comparisons of the measured data and the predicted data. Furthermore, the final settlement of the dike was predicted by using the models, and the stability of the soft foundation of the fishbone-shaped dikes was assessed based on the simulation results of the proposed model. The findings in this paper suggested that the extreme learning machine method could be an effective tool for the soft foundation settlement prediction and assessment of the fishbone-shaped dikes.

scholarly journals Short-Term Electric Load and Price Forecasting Using Enhanced Extreme Learning Machine Optimization in Smart Grids

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 866 ◽  
Author(s):  
Aqdas Naz ◽  
Muhammad Javed ◽  
Nadeem Javaid ◽  
Tanzila Saba ◽  
Musaed Alhussein ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Mean Square Error ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Computational Time ◽  
Major Constituent ◽  
Smart Devices ◽  
Mean Square ◽  
Simulation Results ◽  
Learning Machine

A Smart Grid (SG) is a modernized grid to provide efficient, reliable and economic energy to the consumers. Energy is the most important resource in the world. An efficient energy distribution is required as smart devices are increasing dramatically. The forecasting of electricity consumption is supposed to be a major constituent to enhance the performance of SG. Various learning algorithms have been proposed to solve the forecasting problem. The sole purpose of this work is to predict the price and load efficiently. The first technique is Enhanced Logistic Regression (ELR) and the second technique is Enhanced Recurrent Extreme Learning Machine (ERELM). ELR is an enhanced form of Logistic Regression (LR), whereas, ERELM optimizes weights and biases using a Grey Wolf Optimizer (GWO). Classification and Regression Tree (CART), Relief-F and Recursive Feature Elimination (RFE) are used for feature selection and extraction. On the basis of selected features, classification is performed using ELR. Cross validation is done for ERELM using Monte Carlo and K-Fold methods. The simulations are performed on two different datasets. The first dataset, i.e., UMass Electric Dataset is multi-variate while the second dataset, i.e., UCI Dataset is uni-variate. The first proposed model performed better with UMass Electric Dataset than UCI Dataset and the accuracy of second model is better with UCI than UMass. The prediction accuracy is analyzed on the basis of four different performance metrics: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The proposed techniques are then compared with four benchmark schemes. The comparison is done to verify the adaptivity of the proposed techniques. The simulation results show that the proposed techniques outperformed benchmark schemes. The proposed techniques efficiently increased the prediction accuracy of load and price. However, the computational time is increased in both scenarios. ELR achieved almost 5% better results than Convolutional Neural Network (CNN) and almost 3% than LR. While, ERELM achieved almost 6% better results than ELM and almost 5% than RELM. However, the computational time is almost 20% increased with ELR and 50% with ERELM. Scalability is also addressed for the proposed techniques using half-yearly and yearly datasets. Simulation results show that ELR gives 5% better results while, ERELM gives 6% better results when used for yearly dataset.

scholarly journals Prediction of hydrological time-series using extreme learning machine

2015 ◽  
Vol 18 (2) ◽  
pp. 345-353 ◽  
Author(s):  
Md Atiquzzaman ◽  
Jaya Kandasamy
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Extreme Learning Machine ◽  
Mean Square ◽  
Feed Forward Neural Networks ◽  
Learning Machine ◽  
Hidden Layer

Applying feed-forward neural networks has been limited due to the use of conventional gradient-based slow learning algorithms in training and iterative determination of network parameters. This paper demonstrates a method that partly overcomes these problems by using an extreme learning machine (ELM) which predicts the hydrological time-series very quickly. ELMs, also called single-hidden layer feed-forward neural networks (SLFNs), are able to well generalize the performance for extremely complex problems. ELM randomly chooses a single hidden layer and analytically determines the weights to predict the output. The ELM method was applied to predict hydrological flow series for the Tryggevælde Catchment, Denmark and for the Mississippi River at Vicksburg, USA. The results confirmed that ELM's performance was similar or better in terms of root mean square error (RMSE) and normalized root mean square error (NRMSE) compared to ANN and other previously published techniques, namely evolutionary computation based support vector machine (EC-SVM), standard chaotic approach and inverse approach.

scholarly journals Power load forecasting in energy system based on improved extreme learning machine

2020 ◽  
Vol 38 (4) ◽  
pp. 1194-1211
Author(s):  
Xu-Dong Chen ◽  
Yang Hai-Yue ◽  
Jhang-Shang Wun ◽  
Chien-Hung Wu ◽  
Ching-Hsin Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Extreme Learning Machine ◽  
Nonlinear Dynamic ◽  
Energy System ◽  
Support Vector ◽  
Mean Square ◽  
Power Load ◽  
Learning Machine

Through the accurate prediction of power load, the start and stop of generating units in the power grid can be arranged economically and reasonably. The safety and stability of power grid operation can be maintained. First, chicken swarm optimizer based on nonlinear dynamic convergence factor (NCSO) optimizer is proposed based on chicken swarm optimizer (CSO) optimizer. In NCSO optimizer, nonlinear dynamic inertia weight and levy mutation strategy are introduced. Compared with CSO optimizer, the convergence speed and effect of NCSO optimizer are obviously improved. Second, the random parameters of extreme learning machine (ELM) model are optimized by NCSO optimizer, and NCSOELM model is established to predict the power load. Finally, the NCSO optimization extreme learning machine (NCSOELM) model is used to predict the power load, and compared with back propagation (BP), support vector machine (SVM) and CSO optimization extreme learning machine (CSOELM) model. The experimental results show that the fitting accuracy of NCSOELM model is high, and the determination coefficient r2 is above 90%. And the root mean square error value of the NCSOELM model is 0.87, 0.41, and 0.25 smaller than the root mean square error values of the support vector machine, BP, and CSOELM models, respectively. Experiments show that the model proposed in this study has high fitting effect and low prediction error, which is of positive significance for the realization of economic and safe operation of energy system.

A new constitutive model of a magneto-rheological fluid actuator using an extreme learning machine method

2018 ◽  
Vol 281 ◽  
pp. 209-221 ◽  
Author(s):  
Irfan Bahiuddin ◽  
Saiful A. Mazlan ◽  
Mohd. I. Shapiai ◽  
Seung-Bok Choi ◽  
Fitrian Imaduddin ◽  
...  
Keyword(s):  
Constitutive Model ◽  
Extreme Learning Machine ◽  
Machine Method ◽  
Learning Machine ◽  
Magneto Rheological

The effects of CO2 database on a localized AquaCrop model construction based on the field experiment

2021 ◽  
Author(s):  
fawen li ◽  
chunya song ◽  
hua li
Keyword(s):  
Root Mean Square Error ◽  
Winter Wheat ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Mean Square Error ◽  
North China Plain ◽  
North China ◽  
Mean Square ◽  
Simulation Results

Abstract To examine whether the use of default CO2 database affected the simulation results, this paper built the AquaCrop models of winter wheat based on the measured CO2 database and the default CO2 database, respectively. The models were calibrated with data (2017–2018) and validated with the data (2018–2019) in the North China Plain. The residual coefficient method (CRM), root mean square error (RMSE), normalized root mean square error (NRMSE) and determination coefficient (R2) were used to test the model performance. The results showed that the accuracy of simulation under the two CO2 database were both good. Compared with the default CO2 database, the simulation accuracy under the measured CO2 database had higher accuracy. In order to verify the model further, the simulated values of evapotranspiration, soil water content and measured values were compared and analyzed. The results showed that there were some errors between the measured evapotranspiration and the values of simulation in the filling and waxing period of winter wheat. In general, the simulation values of evapotranspiration were consistent with the measured value at different irrigation levels. The simulated values ​​of the soil water content at the three levels of irrigation were all higher than the measured values, but the simulated results basically reflected the dynamic changes of soil water content throughout the growth period. The model adjustment value of WP*(the normalized water productivity) were a difference under the two CO2 databases, which is one of the reasons for the difference in the simulation results. The results show that in the absence of measured CO2 data, the default CO2 database can be used, which has little influence on the model construction, and the accuracy of the model constructed meets the actual demand. The research results can provide a basis for the establishment of crop models in North China Plain.

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